ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation System

ACTS（AD 临床试验模拟）：为阿尔茨海默病临床试验模拟系统开发先进的信息学方法

• 批准号: 10753675
• 负责人: Jiang Bian
• 金额: $ 115.53万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10753675
• 关键词: Address; Advisory Committees; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; American; Brain Diseases; Caregiver Burden; Case Study; Cause of Death; Characteristics; Clinical; Clinical Data; Clinical Investigator; Clinical Trials; Clinical Trials Design; Cognitive; Cohort Studies; Collection; Communities; Computing Methodologies; Controlled Environment; Data; Data Science; Data Set; Dementia; Development; Disease; Effectiveness; Effectiveness of Interventions; Electronic Health Record; Eligibility Determination; Ensure; Environment; Evaluation; Informatics; Learning; Medical; Medicine; Memory Loss; Metadata; Methodology; Methods; Modeling; Natural Language Processing; Nerve Degeneration; Online Systems; Ontology; Outcome; Outcome Measure; Patients; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Process; Protocols documentation; Randomized, Controlled Trials; Reproducibility; Research; Research Design; Research Personnel; Resources; Safety; Signal Transduction; Societies; Standardization; Structure; Symptoms; System; Testing; Time; Translating; United States; Work; biomedical ontology; clinical practice; compare effectiveness; computable phenotypes; computer framework; cost; data modeling; data standards; database query; design; drug repurposing; effective therapy; follow-up; improved; interoperability; nervous system disorder; novel; open source; outreach; phenotyping algorithm; pilot test; post-market; prototype; randomized, clinical trials; recruit; simulation; study population; success; tool; trait; trial design; unstructured data; usability; virtual; web app; web interface

ABSTRACT Alzheimer's disease and related dementias (AD/ADRD) are the most common neurodegenerative brain disease and characterized by massive loss of memory and learning. AD/ADRD affects more than 6 million Americans and puts a heavy burden on caregivers in society. However, effective treatment of AD/ADRD is still lacking. While randomized clinical trials (RCT) can provide reliable evidence on the effectiveness of interventions, they also have inherent limitations including high cost and long execution time. In addition, RCTs usually are conducted on selected populations and in specialized environments with limited follow up time. Therefore, they could have limitations in generalizability to real-world clinical practice. Clinical trial simulation is becoming an effective approach to assess feasibility, investigate assumptions, and refine study protocols before conducting the actual trials. Increased availability and granularity of real-world data (RWD) such as electronic health record (EHR) and medical claims data along with advances in data science offer untapped opportunities to leverage RWD for trial simulation studies to generate real world evidence (RWE). Nevertheless, there are methodological barriers and informatics challenges in supporting RWD-based trial simulation studies, especially for AD: (1) clinical trials need to be represented using a formal and standard approach (i.e., ontologies) to capture the entire scope of a trial, especially eligibility criteria and outcome measures (i.e., both effectiveness and safety); (2) such formal and standard representation needs to be made interoperable with RWD standards (e.g., common data models) to identify study cohorts and relevant, important patient characteristics (i.e., via computable phenotypes and natural language processing [NLP] methods as rich AD-related information such as cognitive scores often exist in unstructured clinical notes); and (3) comprehensive and reusable pipelines need to be implemented that can seamlessly align with existing large-scale RWD for generating high-quality analytic-ready datasets for AD clinical trial simulation studies. To address these barriers, we propose create and pilot test the ACTS (Alzheimer's disease Clinical Trial Simulation) system, leveraging three large collections of RWD (~20 million patients from the OneFlorida network, UT Physician Clinical Data Research Warehouse, and the Optum’s Clinformatics data). Specifically, we propose to develop novel informatics approaches to represent the entirety of AD trials while considering the connection of RWD (Aim 1), to use both structured and unstructured RWD to develop robust phenotyping algorithms that will render previously incomputable AD study traits computable (Aim 2), and to develop the ACTS web application, which will provide an integrated environment for AD researchers to construct virtual AD trials using an interactive web interface and obtain analytic-ready datasets for trial simulation studies (Aim 3).

抽象的 阿尔茨海默病和相关痴呆（AD/ADRD）是最常见的神经退行性脑部疾病 AD/ADRD 影响着超过 600 万美国人。 然而，AD/ADRD 的有效治疗仍然缺乏。 虽然随机临床试验 (RCT) 可以提供干预措施有效性的可靠证据，但它们 此外，随机对照试验也存在固有的局限性，包括成本高和执行时间长。 因此，他们在特定的环境中对选定的人群进行了有限的随访。 临床试验模拟正在成为一种普遍适用于现实世界临床实践的方法。 在进行研究之前评估可行性、调查假设和完善研究方案的有效方法 提高真实世界数据（RWD）的可用性和粒度，例如电子健康记录。 (EHR) 和医疗索赔数据以及数据科学的进步提供了尚未开发的机会 RWD 用于生成真实世界证据的试验模拟研究 (RWE) 尽管如此，还是有方法论的。 支持基于 RWD 的试验模拟研究的障碍和信息学挑战，特别是 AD：(1) 临床试验需要使用正式和标准的方法（即本体论）来表示，以捕获整个临床试验 试验范围，特别是资格标准和结果衡量标准（即有效性和安全性）；(2) 正式和标准的表示需要与 RWD 标准互操作（例如，通用数据 模型）来识别研究队列和相关的重要患者特征（即通过可计算的表型） 和自然语言处理 [NLP] 方法作为丰富的 AD 相关信息，例如认知分数 (3) 需要实施综合且可重复使用的管道 可以与现有的大规模 RWD 无缝对接，为 AD 生成高质量的分析就绪数据集 为了解决这些障碍，我们建议创建并试点测试 ACTS。 （阿尔茨海默病临床试验模拟）系统，利用三大 RWD 集合（约 2000 万 来自 OneFlorida 网络、UT 医师临床数据研究仓库和 Optum 的患者 具体来说，我们建议开发新的信息学方法来代表整体 AD 试验同时考虑 RWD 的连接（目标 1），使用结构化和非结构化 RWD 来 开发强大的表型分析算法，使以前无法计算的 AD 研究性状变得可计算（目标 2)、开发ACTS网络应用程序，为AD研究人员提供集成环境 使用交互式 Web 界面构建虚拟 AD 试验并获取可供分析的试验数据集 模拟研究（目标 3）。